The working brake lights are only the way to recognize the deceleration of preceding vehicle even if the vehicle is equipped with a highly developed autonomous driving system. The prior recognition of deceleration intent would cause a mitigation of a risk of rear-end collision especially in a high-density and high-speed car-following state. The authors have been developing a system that infers deceleration intention 1.5 s in advance of its driver’s most likely action. However, the previous system that utilizes an unscented Kalman filter (UKF) consists of two isolated processes, a state estimation of vehicle platooning and a prediction of deceleration intention. This separation causes an over- or under-estimate of the intent to worsen the prediction precision. This paper aims to improve the intent inference system by combining both processes within a single procedure. In addition, artificial neural network model and multiple regression model are introduced to estimate both vehicle state variables together with the inferred intention. Numerical analyses showed that the revised model provided more accurate intention compared to the previous system for all five participants. It can be concluded that the integrated state feedback system is appropriately working not only for the state estimation but also the prediction of deceleration intent inference.